8 papers · 1 filter
Aggregation Weighting of Federated Learning via Generalization Bound Estimation
Mingwei Xu, Xiaofeng Cao, Ivor W. Tsang +1
Federated Learning (FL) typically aggregates client model parameters using a weighting approach determined by sample proportions. However, this naive weighting method may lead to u…
Nonparametric Iterative Machine Teaching
Chen Zhang, Xiaofeng Cao, Weiyang Liu +2
In this paper, we consider the problem of Iterative Machine Teaching (IMT), where the teacher provides examples to the learner iteratively such that the learner can achieve fast co…
Policy Dispersion in Non-Markovian Environment
Bohao Qu, Xiaofeng Cao, Jielong Yang +4
Markov Decision Process (MDP) presents a mathematical framework to formulate the learning processes of agents in reinforcement learning. MDP is limited by the Markovian assumption…
Distribution Matching for Machine Teaching
Xiaofeng Cao, Ivor W. Tsang
Machine teaching is an inverse problem of machine learning that aims at steering the student learner towards its target hypothesis, in which the teacher has already known the stude…
Bayesian Active Learning by Disagreements: A Geometric Perspective
Xiaofeng Cao, Ivor W. Tsang
We present geometric Bayesian active learning by disagreements (GBALD), a framework that performs BALD on its core-set construction interacting with model uncertainty estimation. T…
Target-Independent Active Learning via Distribution-Splitting
Xiaofeng Cao, Ivor W. Tsang, Xiaofeng Xu +1
To reduce the label complexity in Agnostic Active Learning (A^2 algorithm), volume-splitting splits the hypothesis edges to reduce the Vapnik-Chervonenkis (VC) dimension in version…